Multi-resolution fingerprint sensor

ABSTRACT

Multi-resolution fingerprint sensors and methods of using the same are provided. The multi-resolution fingerprint sensors each include a portion of the imaging area or region of the sensor that provides higher imaging resolution than the remaining imaging area of the sensor. The area of higher resolution is useful for anti-spoofing purposes, but because only a portion of the sensor is higher-resolution, advantageously it will not impact hardware costs nearly as much as a sensor that was higher resolution over its entire imaging area. Furthermore, the higher resolution area can be down-sampled so there would be no impact on the standard matcher and image processing software stack, which expects a uniform resolution for the entire image acquired.

CROSS-REFERENCES

This application is a continuation of U.S. Non-Provisional applicationSer. No. 15/639,089, filed Jun. 30, 2017, entitled “MULTI-RESOLUTIONFINGERPRINT SENSOR,” which is a continuation of U.S. Non-Provisionalapplication Ser. No. 14/752,728, filed Jun. 26, 2015, entitled“MULTI-RESOLUTION FINGERPRINT SENSOR,” which applications are bothincorporated herein by reference.

FIELD OF THE DISCLOSURE

This disclosure generally relates to biometric recognition and, moreparticularly, to multi-resolution fingerprint sensors and methods ofusing the same.

BACKGROUND OF THE DISCLOSURE

Biometric authentication systems are used for enrolling andauthenticating users of devices incorporating the authenticationsystems. Biometric sensing technology provides a reliable, non-intrusiveway to enroll and verify individual identity for authenticationpurposes.

Fingerprints, like certain other biometric characteristics, are based onunalterable personal characteristics and thus are a reliable mechanismto recognize individuals. There are many potential applications forutilization of biometric and fingerprints sensors. For example,electronic fingerprint sensors may be used to provide access control instationary applications, such as security checkpoints. Electronicfingerprint sensors may also be used to provide access control inportable applications, such as portable computers, personal dataassistants (PDAs), cell phones, gaming devices, navigation devices,information appliances, data storage devices, and the like. Accordingly,some applications, in particular portable applications, may requireelectronic fingerprint sensing systems that are compact, highlyreliable, and inexpensive.

Fingerprint sensors are sometimes referred to as “swipe” sensors or“placement” sensors depending on their principle of operation.Typically, swipe sensors capture an image that is larger than thesensing area by capturing a series of scans of the fingerprint as theuser swipes their finger over the sensing area. A processing system thenreconstructs the scans into a larger swipe image. Since the image isreconstructed from a series of scans, this allows the sensing array tobe made small, even as small as a single scan line, while stillcapturing a larger area image. Placement sensors typically capture animage that corresponds to the size of the sensing area by capturingscans of the fingerprint as it is placed or otherwise held over thesensing area. Usually, placement sensors include a two dimensionalsensor array that can capture a sufficient area of the fingerprint in asingle scan, allowing the fingerprint image to be captured without theuser having to move the finger during the image capture process.

As fingerprint sensors shrink in size, whether for the purpose ofpackaging them into smaller portable devices, to reduce cost, or forother reasons, accurate and usable fingerprint recognition becomes achallenging task. The fingerprint recognition system should capture asufficient area of the fingerprint to discriminate between differentusers. It is possible for a swipe sensor to capture a much larger areaof the fingerprint than the sensor size, allowing the fingerprint sensorto be made small while still capturing a larger area swipe fingerprintimage with enough fingerprint information to easily discriminate betweenusers. Unfortunately, some users find the process of swiping theirfinger over the sensor every time they want to access the system to becumbersome.

Placement sensors provide an attractive solution for many users, sincethey allow the user to simply hold their finger over the sensor.However, there are several technical challenges with small placementsensors that only capture a partial fingerprint image. Because only apartial area of the fingerprint that corresponds to the size of thesensor is captured, the matching process should ideally be tailored toquickly and accurately match based on limited fingerprint information, atask for which conventional matching algorithms based on fullfingerprint images are often poorly equipped. Furthermore, since thesensor is only large enough to capture a partial fingerprint imageduring placement, in ordinary use the user is likely to presentdifferent portions of the same fingerprint on different occasions whenattempting to access the system. The recognition system should ideallybe able to recognize the fingerprint without requiring the user topresent the same small portion of the fingerprint every time.

BRIEF SUMMARY OF THE DISCLOSURE

The present disclosure provides a multi-resolution fingerprint sensorand methods of using the same. The multi-resolution fingerprint sensorincludes a portion of the imaging area or region of the sensor thatprovides higher imaging resolution than the remaining imaging area ofthe sensor. The area of higher resolution would be useful foranti-spoofing purposes, but because only a portion of the sensor ishigher-resolution, advantageously it will not impact hardware costsnearly as much as a sensor that was higher resolution over its entireimaging area. Furthermore, in some embodiments, the higher resolutionarea can be down-sampled so there would be no impact on the standardmatcher and image processing software stack, which typically expects auniform resolution for the entire image acquired.

According to an embodiment, a method of processing an image obtainedwith a multi-resolution biometric sensor is provided. The sensorincludes a first sensing region and second sensing region, the firstsensing region having a resolution lower than a resolution of the secondsensing region. The method typically includes acquiring a first image ofan input object, the first image including an area of the input objectimaged by only the second sensing region of the multi-resolution sensor,and acquiring a second image of the input object, the second imageincluding an area of the input object imaged by both the first sensingregion and the second sensing region of the multi-resolution sensor. Themethod also typically includes processing each of the first image andthe second image to determine biometric information of the input object.The first image and second image may be processed separately orsimultaneously. In certain aspects, the first (e.g., lower resolution)sensing region (at least partially) circumscribes the second sensingregion.

In certain aspects, the first image and the second image are imagedseparately in time, wherein the second image is acquired with the second(e.g., higher resolution) sensing region operating in a low resolutionmode such that the second image has a uniform resolution at theresolution corresponding to the first sensing region. In certainaspects, the second region operating in the low resolution mode includesaddressing a portion of address lines of the second sensing region.

In certain aspects, an initial image of the input object is imagedsimultaneously using both the first sensing region and the secondsensing region. In one aspect, the acquiring the first image includescropping the initial image to remove the area of the input object imagedby the first (e.g., lower resolution) sensing region. In another aspect,the acquiring the second image includes adjusting a resolution of thearea of the input object imaged by the second (e.g., higher resolution)sensing region to match the resolution of the first sensing region toproduce a uniform-resolution image. In certain aspects, the adjustingthe resolution includes down-sampling the area of the input objectimaged by the second (e.g., higher resolution) sensing region.

In certain aspects, the input object includes a fingerprint. In certainaspects, the processing includes processing the first image to determinewhether the fingerprint is a legitimate fingerprint, e.g., foranti-spoofing detection. In certain aspects, the processing includesvalidating the fingerprint using the second image.

In certain aspects, the second sensing region of the sensor comprisessensor electrodes disposed at a pitch less than a sensor electrode pitchof sensor electrodes disposed in the first sensing region. In certainaspects, sensor electrodes of the first sensing region are electrically(e.g., ohmically) connected to sensor electrodes of the second sensingregion. In certain aspects, the multi-resolution sensor includes one ofa transcapacitive sensor, an absolute capacitive sensor, an opticalsensor, a thermal sensor, or an ultrasonic sensor.

BRIEF DESCRIPTION OF THE DRAWING(S)

The accompanying drawings incorporated in and forming a part of thespecification illustrate several aspects of the present disclosure and,together with the description, serve to explain the principles of thedisclosure. In the drawings:

FIG. 1 is a block diagram of an exemplary device that includes an inputdevice and processing system, in accordance with an embodiment of thedisclosure;

FIG. 2A is an image of a fingerprint, in accordance with an embodimentof the disclosure;

FIG. 2B is an enhanced image of the fingerprint of FIG. 2A, inaccordance with an embodiment of the disclosure;

FIG. 3 is an illustration of various types of minutiae points of afingerprint, in accordance with an embodiment of the disclosure;

FIG. 4A is an image of a fingerprint, in accordance with an embodimentof the disclosure;

FIG. 4B is a thin-ridge version of the fingerprint of FIG. 4A, inaccordance with an embodiment of the disclosure;

FIG. 5 is an illustration of a user's fingerprint, in accordance with anembodiment of the disclosure;

FIG. 6A illustrates an example of a multi-resolution sensor according toan embodiment including off-chip line sensing traces;

FIG. 6B illustrates an example of a multi-resolution sensor according toan embodiment including individually addressable sensor electrodes orpixels;

FIG. 7 illustrates an example of processing an image or images acquiredusing a multi-resolution sensor according to an embodiment;

FIG. 8 illustrates a flow chart for processing an image obtained with amulti-resolution biometric sensor having a low resolution sensing regionand a high resolution sensing region, where the low resolution sensingregion has a resolution lower than a resolution of the high resolutionsensing region, according to an embodiment; and

FIG. 9 illustrates a flow chart for processing an image obtained with amulti-resolution biometric sensor having a low resolution sensing regionand a high resolution sensing region, where the low resolution sensingregion has a resolution lower than a resolution of the high resolutionsensing region, according to another embodiment.

DETAILED DESCRIPTION OF THE DISCLOSURE

The following detailed description is merely exemplary in nature and isnot intended to limit the disclosure or the application and uses of thedisclosure. Furthermore, there is no intention to be bound by anyexpressed or implied theory presented in the preceding technical field,background, brief summary or the following detailed description.

Various embodiments of the present disclosure provide input devices andmethods that facilitate improved usability.

Turning now to the figures, FIG. 1 is a block diagram of an electronicsystem or device 100 that includes an input device, such as sensor 102,and processing system 104, in accordance with an embodiment of thedisclosure. As used in this document, the term “input device” and“electronic system” (or “electronic device”) broadly refers to anysystem capable of electronically processing information. Somenon-limiting examples of electronic systems include personal computersof all sizes and shapes, such as desktop computers, laptop computers,netbook computers, tablets, web browsers, e-book readers, and personaldigital assistants (PDAs). Additional example electronic devices includecomposite input devices, such as physical keyboards and separatejoysticks or key switches. Further example electronic systems includeperipherals such as data input devices (including remote controls andmice), and data output devices (including display screens and printers).Other examples include remote terminals, kiosks, and video game machines(e.g., video game consoles, portable gaming devices, and the like).Other examples include communication devices (including cellular phones,such as smart phones), and media devices (including recorders, editors,and players such as televisions, set-top boxes, music players, digitalphoto frames, and digital cameras). Additionally, the electronic device100 could be a host or a slave to the sensor 102.

Sensor 102 can be implemented as a physical part of the electronicdevice 100, or can be physically separate from the electronic device100. For example, sensor elements of sensor 102 may be integrated in adisplay device that is itself implemented as a physical part of theelectronic device 100 or communicably coupled with the electronic device100. As appropriate, the sensor 102 may communicate with parts of theelectronic device 100 using any one or more of the followingcommunication interconnections: buses, networks, and other wired orwireless interconnections. Examples include I²C, SPI, PS/2, UniversalSerial Bus (USB), Bluetooth, RF, and IRDA.

Generally, sensor 102 will be utilized as a fingerprint sensor utilizingone or more various electronic fingerprint sensing methods, techniquesand devices to capture a fingerprint image of a user. Generally,fingerprint sensor 102 may utilize any type of technology to capture auser's fingerprint. For example, in certain embodiments, the fingerprintsensor 102 may be an optical, capacitive, thermal, pressure, radiofrequency (RF) or ultrasonic sensor.

In some embodiments, the sensor 102 is a capacitive fingerprint sensor,with the traces that form a 2D grid array, e.g., with rows oftransmitter/receiver traces on one substrate and columns ofreceiver/transmitter traces on the same or a separate substrate, e.g.,laminated together with some form of dielectric between the traces toform a 2D sensor element array.

Furthermore, biometric image sensors, such as fingerprint sensors, aresometimes referred to as “swipe” sensors or “placement” sensorsdepending on their principle of operation. Typically, swipe sensorscapture an image that is larger than the sensing area by capturing aseries of scans of the fingerprint as the user swipes their finger overthe sensing area. In some applications, a processing system mayreconstruct the scans into a larger swipe image. Since the image may bereconstructed from a series of scans, this allows the sensing array tobe made small, even as small as a single scan line, while stillcapturing a larger area image. In some applications, a larger image areacan be stored as a series of scans using a map or mapping function thatcorrelates the various scan images. Placement sensors typically capturean image that corresponds to the size of the sensing area by capturingscans of the fingerprint as it is placed or otherwise held over thesensing area. Usually, placement sensors include a two dimensionalsensor array that can capture a sufficient area of the fingerprint in asingle scan, allowing the fingerprint image to be captured without theuser having to move the finger during the image capture process.

Placement sensors have an active sensing surface or in other terms,sensing area, that is large enough to accommodate a portion of therelevant part of the fingerprint of the finger during a single scan orsensing action. Where the relevant part of the fingerprint is less thanthe full fingerprint, this is referred to herein as a “partial”fingerprint sensor. Partial fingerprint placement sensors can be madevery small and still reliably recognize fingerprints with sophisticatedmatching schemes. In one embodiment of this disclosure, a partialfingerprint sensor is used with a sensing area less than approximately50 square mm. In another embodiment, a partial fingerprint sensor isused with a sensing area less than approximately 30 square mm.Typically, for placement sensors, the finger is held stationary over thesensing area during a measurement. During a fingerprint enrollmentprocess, multiple views of the fingerprint image may be captured.

Generally, swipe sensors can be made smaller in size than placementsensors that capture an equivalent fingerprint area, and require thefinger to be moved over the sensor during a measurement. Typically, thefinger movement will be either 1D in that the finger moves in a singledirection over the sensor surface, or the finger movement can be 2D inthat the finger can move in more than one direction over the sensorsurface during a measurement. In certain embodiments of this disclosure,a placement sensor may be operated in a swipe mode. In theseembodiments, a placement sensor may capture a swipe image by capturing aseries of scans during relative motion between the sensor array and theuser's fingerprint, and the series of scans are reconstructed into alarger area swipe image. In one implementation, the placement sensorcaptures the scans using its entire sensor array. In anotherimplementation, the placement sensor looks to only a subset of pixels inits sensor array, such as one or two scan lines, when capturing theswipe image.

Turning now to the processing system 104 from FIG. 1, basic functionalcomponents of the electronic device 100 utilized during capturing andstoring a user fingerprint image are illustrated. The processing system104 includes a processor 106 (or multiple processors), a memory 108, atemplate storage 110, a power source 112, an output device(s) 114, aninput device(s) 116 and an operating system (OS) 118 hosting anapplication suite 120 and a matcher 122. Each of the processor 106, thememory 108, the template storage 110, the power source 112, the outputdevice(s) 114, the input device(s) 116 and the operating system 118 areinterconnected physically, communicatively, and/or operatively forinter-component communications.

As illustrated, processor(s) 106 is configured to implementfunctionality and/or process instructions for execution withinelectronic device 100 and the processing system 104. For example,processor 106 executes instructions stored in memory 108 or instructionsstored on template storage 110. Memory 108, which may be anon-transitory, computer-readable storage medium, is configured to storeinformation within electronic device 100 during operation. In someembodiments, memory 108 includes a temporary memory, an area forinformation not to be maintained when the electronic device 100 isturned off. Examples of such temporary memory include volatile memoriessuch as random access memories (RAM), dynamic random access memories(DRAM), and static random access memories (SRAM). Memory 108 alsomaintains program instructions for execution by the processor 106.

Template storage 110 comprises one or more non-transitorycomputer-readable storage media. The template storage 110 is generallyconfigured to store enrollment data such as enrollment views forfingerprint images for a user's fingerprint. The template storage 110may further be configured for long-term storage of information. In someexamples, the template storage 110 includes non-volatile storageelements. Non-limiting examples of non-volatile storage elements includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories.

The processing system 104 includes one or more power sources 112 toprovide power to the electronic device 100, and in some embodiments tosensor 102. Non-limiting examples of power source 112 include single-usepower sources, rechargeable power sources, and/or power sourcesdeveloped from nickel-cadmium, lithium-ion, or other suitable material.

The processing system 104 includes one or more input devices 116. Inputdevices 116 are configured to receive input from a user or a surroundingenvironment of the user through tactile, audio, and/or video feedback.Non-limiting examples of input device 116 include a presence-sensitivescreen, a mouse, a keyboard, a voice responsive system, video camera,microphone or any other type of input device. In some examples, apresence-sensitive screen includes a touch-sensitive screen. In certainembodiments, the sensor 102 may be included as an input device 116.

One or more output devices 114 are also included in processing system104. Output devices 114 are configured to provide output to a user usingtactile, audio, and/or video stimuli. Output device 114 may include adisplay screen (e.g., part of the presence-sensitive screen), a soundcard, a video graphics adapter card, or any other type of device forconverting a signal into an appropriate form understandable to humans ormachines. Additional examples of output device 114 include a speakersuch as headphones, a cathode ray tube (CRT) monitor, a liquid crystaldisplay (LCD), or any other type of device that can generateintelligible output to a user.

The processing system 104 also hosts an operating system 118. Theoperating system 118 controls operations of the components of theprocessing system 104. For example, the operating system 118 facilitatesthe interaction of the processor(s) 106, memory 108, template storage110, power source 112, output devices 114 and input devices 116. Theoperating system 118 further hosts the application suite 120. Theapplication suite 120 contains applications utilizing data stored on thememory 108 or the template storage 110 or data collected from inputdevices 112 or the sensor 102 to cause the processing system 104 toperform certain functions.

In certain embodiments, the application suite 120 hosts an enrollerapplication, which functions to capture one or more views of the user'sfingerprint. The views or fingerprint images generally contain a partialor full image of the user's fingerprint. The enrollment applicationinstructs, either explicitly or implicitly, the user to hold or swipetheir finger across the sensor 102 for capturing or acquiring the imageof the fingerprint. After each requested image is captured, theenrollment application typically stores the captured image in thetemplate storage 110. In certain embodiments, the enrollment applicationwill cause the data representing the captured image to undergo furtherprocessing. For instance, the further processing may be to compress thedata representing the captured image such that it does not take as muchmemory within the template storage 110 to store the image.

In certain embodiments, the application suite 120 will also containapplications for authenticating a user of the electronic device 100. Forexample, these applications may be an OS logon authenticationapplication, a screen saver authentication application, a folder/filelock authentication application, an application lock and a passwordvault application. In each of these applications, the individualapplication will cause the operating system 118 to request the user'sfingerprint for an authentication process prior to undertaking aspecific action, such as providing access to the OS 118 during a logonprocess for the electronic device 100. To perform this process, theabove listed applications will utilize the matcher 122 hosted by theoperating system 118.

FIG. 2A illustrates a grayscale fingerprint image that shows variousridges and minutiae of a fingerprint, according to one embodiment. Ascan be seen in FIG. 2A, the image is noisy such that portions of theimage are cloudy and the ridges or contours are broken. FIG. 2B is anenhanced image of the fingerprint of FIG. 2A.

FIG. 3 illustrates various types of fingerprint minutia, according tosome embodiments. Examples of fingerprint minutia include: a bridgepoint between two or more ridges, a dot, an isolated ridge, an endingridge, a bifurcation point, and an enclosure. Other minutia point typesnot shown in FIG. 3 are also within the scope of the disclosure. Eachminutia point in a fingerprint image is associated with a location (intwo dimensions) and an orientation. In some embodiments, the orientationof a minutia point corresponds to the tangent of the ridge going throughthe minutia point.

FIG. 4A illustrates a grayscale fingerprint image that shows variousridges and minutiae of a fingerprint, according to one embodiment. FIG.4B illustrates a thin-ridge version of the grayscale fingerprint imagein FIG. 4A, according to one embodiment. Fingerprint skeletonization,also sometimes referred to as an “edge map,” “edge image,” or “thinnedridge image,” depending on the context, is the process of converting theridge lines in a grayscale fingerprint image (see, for example, theimage in FIG. 4A) to a binary representation, and reducing the width ofbinarized ridge lines to one pixel wide. As can be seen in FIG. 4B, theskeletonized version of the grayscale fingerprint image removes much ofthe noise so that the image is no longer cloudy and the ridge lines areno longer broken.

Additionally, in embodiments where the sensor 102 (see FIG. 1) is apartial fingerprint sensor such as a partial placement sensor, due tothe size of the sensing area of the sensor 102 typically being smallerthan the user's fingerprint area, a multitude of placement images of theuser's fingerprint from the placement sensor 102 may be collected toform the enrollment template such that it adequately describes theuser's fingerprint. As the multitude of placement images are collected,the enroller function of the application suite 120 calls on the matcher122 to relate the placement views with each other such that they can begrouped into an accurate composite of the user's fingerprint.

Relating the various placement views to one another requireslocalization of each placement view, which specifies a location withinthe user's fingerprint of an individual placement view. In certainembodiments, to assist in localization, the placement image is convertedinto an orientation representation, which specifies a directionalorientation of each ridge of the placement image on a pixel by pixelbasis, or at a lower resolution depending on the coarseness used.Essentially, each pixel in a ridge converted to the orientationrepresentation is represented by data describing the orientation ordirection of the ridge. As an aside, once converted to the orientationrepresentation, the placement image may be referred to as an orientationmap. Further, in certain embodiments, prior to converting to theorientation space, the placement image is converted to a thin ridgeversion of the placement image, and then the thin ridge image isconverted to the orientation space. As additional placement images arecollected and converted to an orientation space, the matcher 122 (seeFIG. 1) will begin to localize each placement view based on ridgeshaving a similar orientation.

FIG. 5 is an illustration of an exemplary embodiment of a user'sfingerprint 500 showing a collection of localized placement views thatform a portion 502 of the user's fingerprint 500. A recently collectedplacement view 504 is illustrated in relation to the portion 502. Theplacement view 504 will be localized in comparison to the portion 502 inorder to determine how it fits along with the rest of portion 502. Inthe illustrated embodiment, placement view 504 overlaps with the portion502 of the user's fingerprint already localized and collected into theenrollment template. However, in other embodiments, the placement view504 may not overlap with the portion 502, or only have a small overlapresulting in a low confidence in the alignment, such that localizing theplacement view 504 by comparing to previous localized portions of thefingerprint becomes more difficult. In addition, multiple disconnectedviews or clusters of views may result, and for which reliable alignmentbecomes difficult without a global reference for localization. Asfurther illustrated, a portion 506 of the user's fingerprint 500 has notbeen presented to the placement sensor 102 and therefore has not beencollected for adding to the enrollment template. However, as theenroller of the application suite 120 and the matcher 122 do not haveprior knowledge of the user's fingerprint 500, the portion 506 of theuser's fingerprint that has not been collected is an unknown. Therefore,in situations where there is no overlap between the placement view 504and the collected portion 502, it becomes difficult to locate where theplacement view 504 should be located in the uncollected portion 506 inrelation to the collected portion 502.

FIGS. 6A-6B illustrate examples of multi-resolution sensors according toembodiments. Each rectangle represents an imaging pixel. For example,each pixel may be implemented using a drive electrode and senseelectrode pair. Electrode elements may be addressed by an electrodearray control circuit (not shown) as is well known. The control circuitmay interface or communicate with various system elements, includingprocessing system 104. FIG. 6A illustrates an example of amulti-resolution sensor 600 according to an embodiment includingoff-chip line sensing traces, e.g. in a capacitive sensor. The tracesmay be formed on a rigid or a flexible substrate. The imaging resolutionof a sensing region is defined by the density of sensor electrodes orpixel elements (“pixels”) in the region. As shown, sensor 600 includes afirst sensing region 610 (shown in dark grey) defined by the columntraces 605 and row traces 606. Column traces 605 and row traces 606 havethe same pitch or spacings in one embodiment, but they may havedifferent spacings in other embodiments. In one embodiment, the spacingsof row and column traces 604 are greater that the spacings of columntraces 605 and row traces 606, so that sensing region 610 has a higherpixel density than surrounding sensing regions 615 and 620. For example,the pitch of the sensor electrodes or pixels of sensing region 610 isless than a pitch of the sensor electrodes or pixels in sensing region620 and sensing region 615. In this manner, the possible imagingresolution of a biometric input object imaged by sensing region 610 ishigher than a possible imaging resolution of the biometric object imagedby sensing region 615 and sensing region 620. Sensing region 615 has ahigher imaging resolution than the imaging resolution of sensing region620 along only one dimension, as shown in light grey. As used herein,sensing region 610 may be referred to as a high resolution region orpatch or a higher resolution region or patch (e.g., relative to sensingregions 615 and 620); whereas, sensing regions 615 and 620 may bereferred to as low resolution region or lower resolution regions.

FIG. 6B illustrates an example of a multi-resolution sensor 650according to an embodiment including individually addressable sensorelectrodes or pixels. As shown, sensor 650 includes a first sensingregion 660 (shown in dark grey) and a second sensing region 670circumscribing the first sensing region 660. The imaging resolution of asensing region is defined by the density of sensor electrodes or pixelsin the region. In one embodiment, sensing region 660 has a higher pixeldensity than surrounding sensing region 670. For example, the pitch ofthe sensor electrodes or pixels of sensing region 660 is less than apitch of the sensor electrodes or pixels in sensing region 670. Hence,the possible imaging resolution of a biometric input object imaged bysensing region 660 is higher than a possible imaging resolution of thebiometric object imaged by sensing region 670. As used herein, sensingregion 660 may be referred to as a high resolution region or patch or ahigher resolution region or patch (in relation to region 670), whereassensing regions 670 may be referred to as a low resolution region or alower resolution region.

In certain embodiments, the high resolution patch of sensors 600 and 650may be positioned anywhere within the entire sensing region of thebiometric sensor. For example, as shown, each high resolution region(610, 660) is shown as positioned in a central region of the sensor(600, 650); however, each high resolution region may be positionedoff-center, e.g., along an edge of the sensor, or proximal an edge orthe sensor, or in a corner of the sensor or proximal a corner of thesensor. Also, each high resolution region (610, 660) is shown as beingsymmetrical, e.g., having a same number of pixels along two orthogonaldirections; however, each high resolution region may be asymmetrical,e.g., having more pixels along one direction than the other, orthogonaldirection. Additionally, each high resolution region (610, 660) is shownas being a single contiguous region of pixels; however, each highresolution region may include multiple separate or distinct regions ofpixels within the sensor region. Further, each high resolution region(610, 660) may include a higher resolution along only one dimension, butnot both dimensions of the two-dimensional sensing region. Additionally,each high resolution region (610, 660) need not be a geometric sub-setof the low resolution region. For example, the first N rows could behigh resolution and the remaining rows could be low resolution.

FIGS. 6A-6B show examples of high resolution sensor regions withspacings of sensor electrodes where the pitch is increased by 2×, e.g.,pitch of pixel elements (e.g., sensor electrodes) in the high resolutionregion is half the pitch of pixel elements in the low resolution region.In certain embodiments, the pitch of the pixel elements in the lowresolution region is an integer multiple of a pitch of the pixelelements in the high resolution region. For example, the pitch of pixelelements (e.g., sensor electrodes) in the high resolution region is 1/Nthe pitch of pixel elements in the low resolution region, where N is aninteger. Practical examples include N=2 (e.g., FIGS. 6A-6B), N=3, N=4, .. . , N=10, etc. N can range as high as the pixel element formingtechnology will allow.

In one embodiment, each high resolution region (610, 660) has an area ofabout 1 mm² or greater and the entire sensing region of each sensor(600, 650) has a total sensing area of about 16 mm² or greater. Incertain embodiments, the resolution of the low resolution region willhave a pitch about 300 dpi (pixels per inch) or higher, and theresolution of the high resolution region will have a pitch about 500 dpior greater. In some examples, for a placement sensor, the total sensingarea can range from about 12 mm² to 100 mm², with example sizes 4 mm×3mm, 10 mm×4 mm or 10 mm×10 mm. In some cases, a sensor area of 4 mm² isa very small and may be an impractical size. In some examples, for aswipe sensor, 4 mm² is a reasonable value since it could be, forexample, about 8 mm wide×0.5 mm high. In some examples, the highresolution region is about 1 mm² to 4 mm².

As one example, consider a sensor having a total sensing area of about100 mm² or (10 mm×10 mm), with 363 dpi (or pixels per inch), meaning a70 um pitch. A 10 mm wide sensing area has roughly 142 pixels along itswidth. If for 2 mm the resolution is increased by a factor of 2 to 726dpi, there would be a maximum of 114+57=171 pixels along the width. Afully high-resolution 726 dpi part, on the other hand, would require 284pixels. Such a sensor provides advantages from an I/O point of view, butwithout the cost and complexity required to produce, and to processsignals from, a full resolution sensor. For example, if one were tobuild a sensor with, say 1000 dpi, the hardware costs would get muchhigher, e.g., due to increase in number of interconnects (I/O pins) tothe silicon. But if a small patch of higher resolution is used, thenonly a few more I/Os are needed. Also gained is an area ofhigh-resolution that can be used for anti-spoofing purposes. Forexample, an anti-spoofing application (in application suite 120) can usethe higher resolution image to detect micro-features such as pores andspoof material defects to identify fake fingers.

In certain embodiments, an image for matching and anti-spoofing purposescould be acquired and processed in multiple ways. For example, in oneembodiment, with reference to sensor 600 or sensor 650 and FIG. 7, amultiple-resolution image 710 of a biometric input object (such as afingerprint) is acquired such that the image includes a first image areacorresponding to the low resolution sensing region of the sensor and asecond image area corresponding to the high resolution sensing region ofthe sensor. The image 710 includes multiple (in this case two) nativeresolutions, meaning most of the image is at one resolution and theimage region corresponding to the high resolution patch is at a higherresolution. In this embodiment, the high res patch region 730 of theacquired image 710 may be cropped and used for anti-spoofing. Thiscropped image 730 may be stored and used later, or it may be providedimmediately to the ant-spoofing software module. Alternately, the entireimage may be provided to the ant-spoofing software, which may restrictits processing to only the high-resolution patch region of the image,e.g., effectively ignoring the remainder, low resolution portion of theimage. Next, the original multi-resolution image 710 is processed toadjust the resolution of the higher resolution region to match theresolution of the lower resolution region of the image. For example, thehigher resolution patch region may be down-sampled or averaged so thatone full-size image 720 at the lower resolution is produced. In thismanner, the downstream software (e.g., matcher or other softwareapplication) would need no modification.

In another embodiment, images 720 and 730 are acquired in two steps. Inthe first step, a certain firing mechanism is used to address pixel toobtain a full-size image 720 at a uniform resolution corresponding tothe lower resolution of the low resolution region. For example, if thehigh resolution patch has a 2× resolution relative to the low resolutionregion, then every other row and column in the patch area is addressed.The resulting lower resolution image 720 could be used for matchingpurposes. In the second step, a high-resolution image 730 is acquired injust the patch area, e.g., only the high resolution patch region pixelelements are addressed. This high resolution image could be sent to thehost processing system 104 for anti-spoofing use.

FIG. 8 illustrates a flow chart 800 for processing an image obtainedwith a multi-resolution biometric sensor (600, 650) having a lowresolution sensing region (620, 670) and a high resolution sensingregion (610, 660), where the low resolution sensing region has aresolution lower than a resolution of the high resolution sensingregion, according to an embodiment. At step 810, an initial prompt isprovided to the user to begin the fingerprint acquisition process. Forexample, the user may be explicitly instructed to touch or swipe theuser's finger on the sensor 102. At step 820, a first image 710 of theuser's fingerprint is acquired by processing system 104 using sensor 102(e.g., sensor 600 or 650). The first image 710 (and any subsequentimages) may be stored to memory 108 or template storage 110 or elsewherein the system. The first image includes a first image area correspondingto the low resolution sensing region (620, 670) of the sensor and asecond image area corresponding to the high resolution sensing region(610, 660) of the sensor, wherein the first image area has a firstresolution lower than a second resolution of the second image area. Thefirst image 710 may include some but not all of the user's fingerprintpattern.

At step 830, a high resolution region of the image is determined, e.g.,for later use by an anti-spoofing application. In one embodiment, thefirst (low resolution) image area of the image is removed, e.g.,cropped, to produce a reduced image 730 comprising only the second (highresolution) image area. Alternatively, the second image area is taggedor identified in the first image, e.g., by starting row and column andending row and column, so that a processing application (e.g.,anti-spoofing application) may easily use only the high resolution areaif desired. At step 840, a substantially uniform-resolution image (e.g.,low resolution) 720 is produced. The second (high) resolution of thesecond image area (corresponding to high resolution patch) in the image710 is adjusted to match the first resolution to produce a substantiallyuniform-resolution image 720. In one embodiment, the resolution isadjusted by down-sampling or decimating pixels of the second, higherresolution image area to match the resolution in the lower resolutionimage area. Alternatively, the resolution is adjusted by averaging pixelvalues of the second image area.

At step 850, the uniform resolution image 720 and the high resolutionpatch image 730 (or the dual resolution image 710, including patchregion identification information) are provided to the software suite120 for processing. For example, matcher 122 may use the uniformresolution image 720 to determine whether biometric information in theimage matches biometric information stored in a matching database, so asto validate or authenticate the fingerprint information. Anti-spoofingsoftware may process the high resolution patch image 730 (or the dualresolution image 710, including patch region identification information)to determine whether the biometric information included in the image isa legitimate fingerprint image. For example, the anti-spoofingapplication may identify micro-features and/or defects of thefingerprint within the image 730. Results of the processing may bedisplayed to the user and/or the user may be otherwise alerted that theresults were successful or unsuccessful. For example, the user may beshown an image of the fingerprint (with fake biometric data to preservebiometric security) or the user may be taken to a specificpost-authentication page, or instructed to re-input the fingerprint, oralerted that there are other problems.

FIG. 9 illustrates a flow chart 900 for processing an image obtainedwith a multi-resolution biometric sensor (600, 650) having a lowresolution sensing region (620, 670) and a high resolution sensingregion (610, 660), where the low resolution sensing region has aresolution lower than a resolution of the high resolution sensingregion, according to an embodiment. At step 910, an initial prompt isprovided to the user to begin the fingerprint acquisition process. Forexample, the user may be explicitly instructed to touch or swipe theuser's finger on the sensor 102. At step 920, a first image of theuser's fingerprint is acquired by processing system 104 using sensor 102(e.g., sensor 600, 650). The first image (and any subsequent images) maybe stored to memory 108 or template storage 110 or elsewhere in thesystem. The first image includes a first image area corresponding toonly the high resolution sensing region (610, 660) of the sensor. Forexample, only the pixels corresponding to the high resolution region areaddressed in step 920. At step 930, a second image of the input objectis acquired using both the high resolution sensing region and the lowresolution sensing region of the multi-resolution sensor. Here, thesecond image is acquired with the high resolution sensing regionoperating in a low resolution mode such that the second image has auniform resolution corresponding to the low resolution of the lowresolution sensing region. For example, in one embodiment, where thepitch of the high resolution patch is a factor N times the pitch of thelow resolution region, every Nth pixel of the high resolution region maybe addressed as all pixels of the low resolution are addressed.

At step 940, the first image (high resolution only) and the second image(low resolution) are provided to the software suite 120 for processing.For example, matcher 122 may use the second image to determine whetherbiometric information in the image matches biometric information storedin a matching database, so as to validate or authenticate thefingerprint information. Anti-spoofing software may process the first(high resolution) image to determine whether the biometric informationincluded in the image is a legitimate fingerprint image. For example,the anti-spoofing application may identify micro-features and/or defectsof the fingerprint within the first image of the fingerprint.

Note that the embodiments herein are applicable to any image sensorregardless of sensing technology. Also, it is simplest to make the highresolution an integer multiple of the lower one, but that is notstrictly necessary. It should also be appreciated to one skilled in theart that the systems and methods described herein with regard tofingerprint sensing are equally applicable to other biometric patternsensing modalities using small sensors or which may require repeatedbiometric pattern entry. For example, other biometric patterns mayinclude, among other possibilities, iris patterns, palm prints, veinpatterns, and faces.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and “at least one” andsimilar referents in the context of describing the embodiments(especially in the context of the following claims) are to be construedto cover both the singular and the plural, unless otherwise indicatedherein or clearly contradicted by context. The use of the term “at leastone” followed by a list of one or more items (for example, “at least oneof A and B”) is to be construed to mean one item selected from thelisted items (A or B) or any combination of two or more of the listeditems (A and B), unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinthe range, unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein can be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe embodiments of the disclosure unless otherwise claimed. No languagein the specification should be construed as indicating any non-claimedelement as essential to the practice the embodiments of the disclosure.

Various embodiments are described herein. Variations of thoseembodiments may become apparent to those of ordinary skill in the artupon reading the foregoing description. The inventors expect skilledartisans to employ such variations as appropriate, and the inventorsintend for the embodiments to be practiced otherwise than asspecifically described herein. Accordingly, this specification includesall modifications and equivalents of the subject matter recited in theclaims appended hereto as permitted by applicable law. Moreover, anycombination of the above-described elements in all possible variationsthereof is encompassed by the disclosure unless otherwise indicatedherein or otherwise clearly contradicted by context.

The invention claimed is:
 1. A method of processing an image obtainedwith a biometric sensor having a sensing region, the method comprising:acquiring with the biometric sensor an image of an input object in thesensing region, the image having a first resolution; providing the imagefor first image processing to determine whether the input object is alegitimate biometric object; adjusting the image to a second resolutionlower than the first resolution to produce a reduced-resolution image;and providing the reduced-resolution image for second image processingto validate the biometric object.
 2. The method of claim 1, wherein theinput object includes a fingerprint.
 3. The method of claim 2, whereinthe first image processing processes the image to determine whether thefingerprint is a legitimate fingerprint.
 4. The method of claim 3,wherein the first image processing identifies micro features and/ordefects of the fingerprint within the image.
 5. The method of claim 2,wherein the second image processing processes the reduced-resolutionimage to validate the fingerprint.
 6. The method of claim 5, wherein thesecond image processing determines whether biometric information in thereduced-resolution image matches biometric information stored in amatching database.
 7. The method of claim 1, wherein the adjusting theimage includes down-sampling, decimating, and/or averaging pixels of theimage.
 8. The method of claim 1, wherein the biometric sensor includesone of a transcapacitive sensor, an absolute capacitive sensor, anoptical sensor, a thermal sensor, or an ultrasonic sensor.
 9. Abiometric input device comprising: a biometric sensor having a sensingregion; and a processing system configured to determine biometricinformation from an input object in the sensing region, wherein theprocessing system is configured to: receive an image acquired by thebiometric sensor of the input object in the sensing region, the imagehaving a first resolution; perform anti-spoofing image processing on theimage to determine whether the input object is a legitimate biometricobject; adjust the image to a second resolution lower than the firstresolution to produce a reduced-resolution image; and perform matchingimage processing on the reduced-resolution image to validate thebiometric object.
 10. The biometric input device of claim 9, wherein theinput object includes a fingerprint.
 11. The biometric input device ofclaim 10, wherein the anti-spoofing image processing includes processingthe image to determine whether the fingerprint is a legitimatefingerprint.
 12. The method of claim 11, wherein the anti-spoofing imageprocessing includes identifying micro features and/or defects of thefingerprint within the image.
 13. The method of claim 10, wherein thematching image processing includes processing the reduced-resolutionimage to validate the fingerprint.
 14. The method of claim 13, whereinthe matching image processing includes determining whether biometricinformation in the reduced-resolution image matches biometricinformation stored in a matching database.
 15. The method of claim 9,wherein the processing system adjusts the image by down-sampling,decimating, and/or averaging pixels of the image.
 16. The biometricinput device of claim 9, wherein the biometric sensor comprises one of atranscapacitive sensor, an absolute capacitive sensor, an opticalsensor, a thermal sensor, or an ultrasonic sensor.
 17. A non-transitorycomputer readable medium storing instructions for processing an imageobtained with a biometric sensor, wherein when executed by a processingsystem, the instructions cause the processing system to: receive fromthe biometric sensor an image of an input object in a sensing region ofthe biometric sensor, the image having a first resolution; performanti-spoofing image processing on the image to determine whether theinput object is a legitimate biometric object; adjust the image to asecond resolution lower than the first resolution to produce areduced-resolution image; and perform matching image processing on thereduced-resolution image to validate the biometric object.
 18. Thenon-transitory computer readable medium of claim 17, wherein the inputobject includes a fingerprint.
 19. The non-transitory computer readablemedium of claim 18, wherein the anti-spoofing image processing includesprocessing the image to determine whether the fingerprint is alegitimate fingerprint, and wherein the matching image processingincludes processing the reduced-resolution image to validate thefingerprint.
 20. The non-transitory computer readable medium of claim17, wherein the instructions to adjust include instructions to adjustthe image by down-sampling, decimating, and/or averaging pixels of theimage.